Network lifetime is a key issue in wireless sensor networks where sensor nodes, distributed typically in remote/hostile sensing areas, are powered by finite energy batteries which are not easily replaced/recharged. Depletion of these finite energy batteries can result in a change in network topology or in the end of network life itself. Hence, prolonging the life of wireless sensor networks is important. Energy consumed in wireless sensor nodes can be for the purpose of i) sensing functions, ii) processing/computing functions, and ii) communication functions. For example, energy consumed by the transmit and receive electronics constitute the energy expended for communication functions. Our focus in this thesis is on the efficient use of energy for sensing. In particular, we are concerned with energy efficient algorithms for scheduling the sensing activity of sensor nodes. By scheduling the sensing activity we mean when to activate a sensor node for sensing (active mode) and when to keep it idle (sleep mode). The novel approach we adopt in this thesis to achieve efficient scheduling of sensing activity is an information coverage approach, rather than the widely adopted physical coverage approach. In the physical coverage approach, a point is said to be covered by a sensor node if that point lies within the physical coverage range (or the sensing radius) of that sensor, which is the maximum distance between the sensor and the point up to which the sensor can sense with acceptable accuracy. Information coverage, on the other hand, exploits cooperation among multiple sensors to accurately sense a point even if that point falls outside the physical coverage range of all the sensors. In this thesis, we address the question of how to schedule the activity of various sensor nodes in the network to enhance network lifetime using information coverage. In the first part of the thesis, we are concerned with scheduling of sensor nodes for sensing point targets using information coverage – example of a point-target being temperature or radiation level at a source or point that needs to be monitored. Defining a set of sensor nodes which collectively can sense a point-target accurately as an information cover, we propose an algorithm to obtain Disjoint Set of Information Covers (DSIC) that can sense multiple point-targets in a given sensing area. Being disjoint, the resulting information covers in the proposed algorithm allow a simple round-robin schedule of sensor activation (i.e., activate the covers sequentially). We show that the covers obtained using the proposed DSIC algorithm achieve longer network life compared to the covers obtained using an Exhaustive-Greedy-Equalized Heuristic (EGEH) proposed recently in the literature. We also present a detailed complexity comparison between the DSIC and EGEH algorithms. In the second part of the thesis, we extend the point target sensing problem in the first part to a full area sensing problem, where we are concerned with energy efficient ‘area-monitoring’ using information coverage. We refer to any set of sensors that can collectively sense all points in the entire area-to-monitor as a full area information cover. We first propose a low-complexity heuristic algorithm to obtain full area information covers. Using these covers, we then obtain the optimum schedule for activating the sensing activity of various sensors that maximizes the sensing lifetime. The optimum schedules obtained using the proposed algorithm is shown to achieve significantly longer sensing lifetimes compared to those achieved using physical coverage. Relaxing the full area coverage requirement to a partial area coverage requirement (e.g., 95% of area coverage as adequate instead of 100% area coverage) further enhances the lifetime. The algorithms proposed for the point targets and full area sensing problems in the first two parts are essentially centralized algorithms. Decentralized algorithms are preferred in large networks. Accordingly, in the last part of the thesis, we propose a low-complexity, distributed sensor scheduling algorithm for full area sensing using information coverage. This distributed algorithm is shown to result in significantly longer sensing lifetimes compared to those achieved using physical coverage.

In this thesis, we consider stochastic systems which arise in different real-world application contexts. The first problem we consider is based on product adoption and pricing. A monopolist selling a product has to appropriately price the product over time in order to maximize the aggregated profit. The demand for a product is uncertain and is influenced by a number of factors, some of which are price, advertising, and product technology. We study the influence of price on the demand of a product and also how demand affects future prices. Our approach involves mathematically modelling the variation in demand as a function of price and current sales. We present a simulation-based algorithm for computing the optimal price path of a product for a given period of time. The algorithm we propose uses a smoothed-functional based performance gradient descent method to find a price sequence which maximizes the total profit over a planning horizon. The second system we consider is in the domain of sensor networks. A sensor network is a collection of autonomous nodes, each of which senses the environment. Sensor nodes use energy for sensing and communication related tasks. We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting(EH) source. Nodes periodically sense a random field and generate data, which is stored in their respective data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in a buffer. The nodes require energy for transmission of data and and they receive the energy for this purpose from the EH source. There is a need for efficiently sharing the stored energy in the EH source among the nodes in the system, in order to minimize average delay of data transmission over the long run. We formulate this problem in the framework of average cost infinite-horizon Markov Decision Processes[3],[7]and provide algorithms for the same.

Wireless sensor networks(WSNs) have a diverse set of applications such as military surveillance, health and environmental monitoring, and home automation. Sensor nodes are equipped with pre-charged batteries, which drain out when the nodes sense, process, and communicate data. Eventually, the nodes of the WSN die and the network dies. Energy harvesting(EH) is a green alternative to solve the limited lifetime problem in WSNs. EH nodes recharge their batteries by harvesting ambient energy such as solar, wind, and radio energy. However, due to the randomness in the EH process and the limited amounts of energy that can be harvested, the EH nodes are often intermittently available. Therefore, even though EH nodes live perpetually, they do not cater to the network continuously. We focus on the energy-efficient design of WSNs that incorporate EH, and investigate the new design trade-offs that arise in exploiting the potentially scarce and random energy arrivals and channel fading encountered by the network. To this end, firstly, we compare the performance of conventional, all-EH, and hybrid WSNs, which consist of both conventional and EH nodes. We then study max function computation, which aims at energy-efficient data aggregation, in EH WSNs. We first argue that the conventional performance criteria used for evaluating WSNs, which are motivated by lifetime, and for evaluating EH networks are at odds with each other and are unsuitable for evaluating hybrid WSNs. We propose two new and insightful performance criteria called the k-outage and n-transmission durations to evaluate and compare different WSNs. These criteria capture the effect of the battery energies of the nodes and the channel fading conditions on the network operations. We prove two computationally-efficient bounds for evaluating these criteria, and show their use in a cost-constrained deployment of a WSN involving EH nodes. Next, we study the estimation of maximum of sensor readings in an all-EH WSN. We analyze the mean absolute error(MAE) in estimating the maximum reading when a random subset of the EH nodes periodically transmit their readings to the fusion node. We determine the optimal transmit power and the number of scheduled nodes that minimize the MAE. We weigh the benefits of the availability of channel information at the nodes against the cost of acquiring it. The results are first developed assuming that the readings are transmitted with infinite resolution. The new trade-offs that arise when quantized readings are instead transmitted are then characterized.Our results hold for any distribution of sensor readings, and for any stationary and ergodic EH process.

Many sequential decision making problems under uncertainty arising in engineering, science and economics are often modelled as Markov Decision Processes (MDPs). In the setting of MDPs, the goal is to and a state dependent optimal sequence of actions that minimizes a certain long-term performance criterion. The standard dynamic programming approach to solve an MDP for the optimal decisions requires a complete model of the MDP and is computationally feasible only for small state-action MDPs. Reinforcement learning (RL) methods, on the other hand, are model-free simulation based approaches for solving MDPs. In many real world applications, one is often faced with MDPs that have large state-action spaces whose model is unknown, however, whose outcomes can be simulated. In order to solve such (large) MDPs, one either resorts to the technique of function approximation in conjunction with RL methods or develops application specific RL methods. A solution based on RL methods with function approximation comes with the associated problem of choosing the right features for approximation and a solution based on application specific RL methods primarily relies on utilizing the problem structure. In this thesis, we investigate the problem of choosing the right features for RL methods based on function approximation as well as develop novel RL algorithms that adaptively obtain best features for approximation. Subsequently, we also develop problem specie RL methods for applications arising in the areas of wireless sensor networks and road traffic control. In the first part of the thesis, we consider the problem of finding the best features for value function approximation in reinforcement learning for the long-run discounted cost objective. We quantify the error in the approximation for any given feature and the approximation parameter by the mean square Bellman error (MSBE) objective and develop an online algorithm to optimize MSBE. Subsequently, we propose the first online actor-critic scheme with adaptive bases to find a locally optimal (control) policy for an MDP under the weighted discounted cost objective. The actor performs gradient search in the space of policy parameters using simultaneous perturbation stochastic approximation (SPSA) gradient estimates. This gradient computation however requires estimates of the value function of the policy. The value function is approximated using a linear architecture and its estimate is obtained from the critic. The error in approximation of the value function, however, results in sub-optimal policies. Thus, we obtain the best features by performing a gradient descent on the Grassmannian of features to minimize a MSBE objective. We provide a proof of convergence of our control algorithm to a locally optimal policy and show numerical results illustrating the performance of our algorithm. In our next work, we develop an online actor-critic control algorithm with adaptive feature tuning for MDPs under the long-run average cost objective. In this setting, a gradient search in the policy parameters is performed using policy gradient estimates to improve the performance of the actor. The computation of the aforementioned gradient however requires estimates of the differential value function of the policy. In order to obtain good estimates of the differential value function, the critic adaptively tunes the features to obtain the best representation of the value function using gradient search in the Grassmannian of features. We prove that our actor-critic algorithm converges to a locally optimal policy. Experiments on two different MDP settings show performance improvements resulting from our feature adaptation scheme. In the second part of the thesis, we develop problem specific RL solution methods for the two aforementioned applications. In both the applications, the size of the state-action space in the formulated MDPs is large. However, by utilizing the problem structure we develop scalable RL algorithms. In the wireless sensor networks application, we develop RL algorithms to find optimal energy management policies (EMPs) for energy harvesting (EH) sensor nodes. First, we consider the case of a single EH sensor node and formulate the problem of finding an optimal EMP in the discounted cost MDP setting. We then propose two RL algorithms to maximize network performance. Through simulations, our algorithms are seen to outperform the algorithms in the literature. Our RL algorithms for the single EH sensor node do not scale when there are multiple sensor nodes. In our second work, we consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the -greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through numerical experiments, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method. In the context of road traffic control, optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users. This problem is hard to solve when simultaneously considering all the junctions in the road network. So, we propose a decentralized multi-agent reinforcement learning (MARL) algorithm for solving this problem by considering each junction in the road network as a separate agent (controller) to obtain dynamic TSC policies. We propose two approaches to minimize the average delay. In the first approach, each agent decides the signal duration of its phases in a round-robin (RR) manner using the multi-agent Q-learning algorithm. We show through simulations over VISSIM (microscopic traffic simulator) that our round-robin MARL algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm over two real road networks. In the second approach, instead of optimizing green light duration, each agent optimizes the order of the phase sequence. We then employ our MARL algorithms by suitably changing the state-action space and cost structure of the MDP. We show through simulations over VISSIM that our non-round robin MARL algorithms perform significantly better than the FST, SAT and the round-robin MARL algorithms based on the first approach. However, on the other hand, our round-robin MARL algorithms are more practically viable as they conform with the psychology of road users.

Ce travail de thèse s’inscrit dans le domaine de l’informatique affective et plus précisément de l’intelligence artificielle et de l’exploration d’architecture. L’objectif de ce travail est de concevoir un système complet de détection des émotions en utilisant des signaux physiologiques. Ce travail se place donc à l’intersection de l’informatique pour la définition d’algorithme de détection des émotions et de l’électronique pour l’élaboration d’une méthodologie d’exploration d’architecture et pour la conception de nœuds de capteurs. Dans un premier temps, des algorithmes de détection multimodale et instantanée des émotions ont été définis. Deux algorithmes de classification KNN puis SVM, ont été implémentés et ont permis d’obtenir un taux de reconnaissance des émotions supérieurs à 80%. Afin de concevoir un tel système alimenté sur pile, un modèle analytique d’estimation de la consommation à haut niveau d’abstraction a été proposé et validé sur une plateforme réelle. Afin de tenir compte des contraintes utilisateurs, un outil de conception et de simulation d’architecture d’objets connectés pour la santé a été développé, permettant ainsi d’évaluer les performances des systèmes avant leur conception. Une architecture logicielle/matérielle pour la collecte et le traitement des données satisfaisant les contraintes applicatives et utilisateurs a ainsi été proposée. Doté de cette architecture, des expérimentations ont été menées pour la Mnémothérapie musicale. EMOTICA est un système complet de détection des émotions utilisant des signaux physiologiques satisfaisant les contraintes d’architecture, d’application et de l’utilisateur. / This thesis work is part of the field of affective computing and more specifically artificial intelligence and architectural exploration. The goal of this work is to design a complete system of emotions detection using physiological signals. This work is therefore situated at the intersection of computer science for the definition of algorithm of detection of emotions and electronics for the development of an architecture exploration methodology for the design of sensor nodes. At first, algorithms for multimodal and instantaneous detection of emotions were defined. Two algorithms of classification KNN then SVM, were implemented and made it possible to obtain a recognition rate of the emotions higher than 80%. To design such a battery-powered system, an analytical model for estimating the power consumption at high level of abstraction has been proposed and validated on a real platform. To consider user constraints, a connected object architecture design and simulation tool for health has been developed, allowing the performance of systems to be evaluated prior to their design. Then, we used this tool to propose a hardware/software architecture for the collection and the processing of the data satisfying the architectural and applicative constraints. With this architecture, experiments have been conducted for musical Mnemotherapy. EMOTICA is a complete system for emotions detection using physiological signals satisfying the constraints of architecture, application and user.

This thesis presents the development of a Sensor Tower Platform (STP) comprised of an array of Passive Infra-Red (PIR) sensors along with a classification algorithm that enables the STP to distinguish between human intrusion, animal intrusion and clutter arising from wind-blown vegetative movement in an outdoor environment. The research was motivated by the aim of exploring the potential use of wireless sensor networks (WSNs) as an early-warning system to help mitigate human-wildlife conflicts occurring at the edge of a forest. While PIR sensors are in commonplace use in indoor settings, their use in an outdoor environment is hampered by the fact that they are prone to false alarms arising from wind-blown vegetation. Every PIR sensor is made up of one or more pairs of pyroelectric pixels arranged in a plane, and the orientation of interest in this thesis is one in which this plane is a vertical plane, i.e., a plane perpendicular to the ground plane. The intersection of the Field Of View (FOV) of the PIR sensor with a second vertical plane that lies within the FOV of the PIR sensor, is called the virtual pixel array (VPA). The structure of the VPA corresponding to the plane along which intruder motion takes place determines the form of the signal generated by the PIR sensor. The STP developed in this thesis employs an array of PIR sensors designed so as to result in a VPA that makes it easier to discriminate between human and animal intrusion while keeping to a small level false alarms arising from vegetative motion. The design was carried out in iterative fashion, with each successive iteration separated by a lengthy testing phase. There were a total of 5 design iterations spanning a total period of 14 months. Given the inherent challenges involved in gathering data corresponding to animal intrusion, the testing of the SP was carried out both using real-world data and through simulation. Simulation was carried out by developing a tool that employed animation software to simulate intruder and animal motion as well as some limited models of wind-blown vegetation. More specifically, the simulation tool employed 3-dimensional models of intruder and shrub motion that were developed using the popular animation software Blender. The simulated output signal of the PIR sensor was then generated by calculating the area of the 3-dimensional intruder when projected onto the VPA of the STP. An algorithm for efficiently calculating this to a good degree of approximation was implemented in Open Graphics Library (OpenGL). The simulation tool was useful both for evaluating various competing design alternatives as well as for developing an intuition for the kind of signals the SP would generate without the need for time-consuming and challenging animal-motion data collection. Real-world data corresponding to human motion was gathered on the campus of the Indian Institute of Science (IISc), while animal data was recorded at a dog-trainer facility in Kengeri as well as the Bannerghatta Biological Park, both located in the outskirts of Bengaluru. The array of PIR sensors was designed so as to result in a VPA that had good spatial resolution. The spatial resolution capabilities of the STP permitted distinguishing between human and animal motion with good accuracy based on low-complexity, signal-energy computations. Rejecting false alarms arising from vegetative movement proved to be more challenging. While the inherent spatial resolution of the STP was very helpful, an alternative approach turned out to have much higher accuracy, although it is computationally more intensive. Under this approach, the intruder signal, either human or animal, was modelled as a chirp waveform. When the intruder moves along a circular arc surrounding the STP, the resulting signal is periodic with constant frequency. However, when the intruder moves along a more likely straight-line path, the resultant signal has a strong chirp component. Clutter signals arising from vegetative motion does not exhibit this chirp behavior and an algorithm that exploited this difference turned in a classification accuracy in excess of 97%.

<div>Energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous IoT nodes. Several scenarios including In-Sensor-Analytics (ISA), Collaborative Intelligence (CI) and Context-Aware-Switching (CAS) of the cluster-head during CI have been explored to trade-off the energies required for communication and computation in a wireless sensor network deployed in a mesh for multi-sensor measurement. A real-time co-optimization algorithm was developed for minimizing the energy consumption in the network for maximizing the overall battery lifetime of individual nodes.</div><div><br></div><div>The difficulty of achieving the design goals of lifetime, information accuracy, transmission distance, and cost, using traditional battery powered devices has driven significant research in energy-harvested wireless sensor nodes. This challenge is further amplified by the inherent power intensive nature of long-range communication when sensor networks are required to span vast areas such as agricultural fields and remote terrain. Solar power is a common energy source is wireless sensor nodes, however, it is not reliable due to fluctuations in power stemming from the changing seasons and weather conditions. This paper tackles these issues by presenting a perpetually-powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long range communication by dynamically co-optimizing energy consumption and information transfer, termed as Energy-Information Dynamic Co-Optimization (EICO). This energy-information intelligence is achieved by adaptive duty cycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing event driven communication to minimize loss of information. We show results of continuous monitoring across 1Km without replacing the battery and maintaining an information accuracy of at least 95%.</div><div><br></div><div>Decades of continuous scaling in semiconductor technology has resulted in a drastic reduction in the cost and size of unit computing. This has enabled the design and development of small form factor wearable devices which communicate with each other to form a network around the body, commonly known as the Wireless Body Area Network (WBAN). These devices have found significant application for medical purposes such as reading surface bio-potential signals for monitoring, diagnosis, and therapy. One such device for the management of oropharyngeal swallowing disorders is described in this thesis. Radio wave transmission over air is the commonly used method of communication among these devices, but in recent years Human Body Communication has shown great promise to replace wireless communication for information exchange in a WBAN. However, there are very few studies in literature, that systematically study the channel loss of capacitive HBC for <i>wearable devices</i> over a wide frequency range with different terminations at the receiver, partly due to the need for <i>miniaturized wearable devices</i> for an accurate study. This thesis also measures and explores the channel loss of capacitive HBC from 100KHz to 1GHz for both high-impedance and 50Ohm terminations using wearable, battery powered devices; which is mandatory for accurate measurement of the HBC channel-loss, due to ground coupling effects. The measured results provide a consistent wearable, wide-frequency HBC channel loss data and could serve as a backbone for the emerging field of HBC by aiding in the selection of an appropriate operation frequency and termination.</div><div><br></div><div>Lastly, the power and security benefits of human body communication is demonstrated by extending it to animals (animal body communication). A sub-inch^3, custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation accuracy >99% when compared to traditional wireless communication modalities, with a 50x reduction in power consumption.</div>

Surveillance wireless sensor networks (WSNs) are highly vulnerable to the failure of the base station (BS) because attackers can easily render the network useless for relatively long periods of time by only destroying the BS. The time and effort needed to destroy the BS is much less than that needed to destroy the numerous sensing nodes. Previous works have tackled BS failure by deploying a mobile BS or by using multiple BSs, which requires extra cost. Moreover, despite using the best electronic countermeasures, intrusion tolerance systems and anti-traffic analysis strategies to protect the BSs, an adversary can still destroy them. The new BS cannot trust the deployed sensor nodes. Also, previous works lack both the procedures to ensure network reliability and security during BS failure such as storing then sending reports concerning security threats against nodes to the new BS and the procedures to verify the trustworthiness of the deployed sensing nodes. Otherwise, a new WSN must be re-deployed which involves a high cost and requires time for the deployment and setup of the new WSN. In this thesis, we address the problem of reliable recovery from a BS failure by proposing a new security architecture called Surveillance Security (SurvSec). SurvSec continuously monitors the network for security threats and stores data related to node security, detects and authenticates the new BS, and recovers the stored data at the new BS. SurvSec includes encryption for security-related information using an efficient dynamic secret sharing algorithm, where previous work has high computations for dynamic secret sharing. SurvSec includes compromised nodes detection protocol against collaborative work of attackers working at the same time where previous works have been inefficient against collaborative work of attackers working at the same time. SurvSec includes a key management scheme for homogenous WSN, where previous works assume heterogeneous WSN using High-end Sensor Nodes (HSN) which are the best target for the attackers. SurvSec includes efficient encryption architecture against quantum computers with a low time delay for encryption and decryption, where previous works have had high time delay to encrypt and decrypt large data size, where AES-256 has 14 rounds and high delay. SurvSec consists of five components, which are: 1. A Hierarchical Data Storage and Data Recovery System. 2. Security for the Stored Data using a new dynamic secret sharing algorithm. 3. A Compromised-Nodes Detection Algorithm at the first stage. 4. A Hybrid and Dynamic Key Management scheme for homogenous network. 5. Powerful Encryption Architecture for post-quantum computers with low time delay. In this thesis, we introduce six new contributions which are the followings: 1. The development of the new security architecture called Surveillance Security (SurvSec) based on distributed Security Managers (SMs) to enable distributed network security and distributed secure storage. 2. The design of a new dynamic secret sharing algorithm to secure the stored data by using distributed users tables. 3. A new algorithm to detect compromised nodes at the first stage, when a group of attackers capture many legitimate nodes after the base station destruction. This algorithm is designed to be resistant against a group of attackers working at the same time to compromise many legitimate nodes during the base station failure. 4. A hybrid and dynamic key management scheme for homogenous network which is called certificates shared verification key management. 5. A new encryption architecture which is called the spread spectrum encryption architecture SSEA to resist quantum-computers attacks. 6. Hardware implementation of reliable network recovery from BS failure. The description of the new security architecture SurvSec components is done followed by a simulation and analytical study of the proposed solutions to show its performance.

One of the major applications of wireless sensor networks is to sense accurate and reliable data from the physical environment with or without a priori knowledge of data statistics. To extract accurate data from the physical environment, we investigate spatial data correlation among sensor nodes to develop data accuracy models. We propose three data accuracy models namely Estimated Data Accuracy (EDA) model, Cluster based Data Accuracy (CDA) model and Distributed Cluster based Data Accuracy (DCDA) model with a priori knowledge of data statistics. Due to the deployment of high density of sensor nodes, observed data are highly correlated among sensor nodes which form distributed clusters in space. We describe two clustering algorithms called Deterministic Distributed Clustering (DDC) algorithm and Spatial Data Correlation based Distributed Clustering (SDCDC) algorithm implemented under CDA model and DCDA model respectively. Moreover, due to data correlation in the network, it has redundancy in data collected by sensor nodes. Hence, it is not necessary for all sensor nodes to transmit their highly correlated data to the central node (sink node or cluster head node). Even an optimal set of sensor nodes are capable of measuring accurate data and transmitting the accurate, precise data to the central node. This reduces data redundancy, energy consumption and data transmission cost to increase the lifetime of sensor networks. Finally, we propose a fourth accuracy model called Adaptive Data Accuracy (ADA) model that doesn't require any a priori knowledge of data statistics. ADA model can sense continuous data stream at regular time intervals to estimate accurate data from the environment and select an optimal set of sensor nodes for data transmission to the network. Data transmission can be further reduced for these optimal sensor nodes by transmitting a subset of sensor data using a methodology called Spatio-Temporal Data Prediction (STDP) model under data reduction strategies. Furthermore, we implement data accuracy model when the network is under a threat of malicious attack.

Surveillance wireless sensor networks (WSNs) are highly vulnerable to the failure of the base station (BS) because attackers can easily render the network useless for relatively long periods of time by only destroying the BS. The time and effort needed to destroy the BS is much less than that needed to destroy the numerous sensing nodes. Previous works have tackled BS failure by deploying a mobile BS or by using multiple BSs, which requires extra cost. Moreover, despite using the best electronic countermeasures, intrusion tolerance systems and anti-traffic analysis strategies to protect the BSs, an adversary can still destroy them. The new BS cannot trust the deployed sensor nodes. Also, previous works lack both the procedures to ensure network reliability and security during BS failure such as storing then sending reports concerning security threats against nodes to the new BS and the procedures to verify the trustworthiness of the deployed sensing nodes. Otherwise, a new WSN must be re-deployed which involves a high cost and requires time for the deployment and setup of the new WSN. In this thesis, we address the problem of reliable recovery from a BS failure by proposing a new security architecture called Surveillance Security (SurvSec). SurvSec continuously monitors the network for security threats and stores data related to node security, detects and authenticates the new BS, and recovers the stored data at the new BS. SurvSec includes encryption for security-related information using an efficient dynamic secret sharing algorithm, where previous work has high computations for dynamic secret sharing. SurvSec includes compromised nodes detection protocol against collaborative work of attackers working at the same time where previous works have been inefficient against collaborative work of attackers working at the same time. SurvSec includes a key management scheme for homogenous WSN, where previous works assume heterogeneous WSN using High-end Sensor Nodes (HSN) which are the best target for the attackers. SurvSec includes efficient encryption architecture against quantum computers with a low time delay for encryption and decryption, where previous works have had high time delay to encrypt and decrypt large data size, where AES-256 has 14 rounds and high delay. SurvSec consists of five components, which are: 1. A Hierarchical Data Storage and Data Recovery System. 2. Security for the Stored Data using a new dynamic secret sharing algorithm. 3. A Compromised-Nodes Detection Algorithm at the first stage. 4. A Hybrid and Dynamic Key Management scheme for homogenous network. 5. Powerful Encryption Architecture for post-quantum computers with low time delay. In this thesis, we introduce six new contributions which are the followings: 1. The development of the new security architecture called Surveillance Security (SurvSec) based on distributed Security Managers (SMs) to enable distributed network security and distributed secure storage. 2. The design of a new dynamic secret sharing algorithm to secure the stored data by using distributed users tables. 3. A new algorithm to detect compromised nodes at the first stage, when a group of attackers capture many legitimate nodes after the base station destruction. This algorithm is designed to be resistant against a group of attackers working at the same time to compromise many legitimate nodes during the base station failure. 4. A hybrid and dynamic key management scheme for homogenous network which is called certificates shared verification key management. 5. A new encryption architecture which is called the spread spectrum encryption architecture SSEA to resist quantum-computers attacks. 6. Hardware implementation of reliable network recovery from BS failure. The description of the new security architecture SurvSec components is done followed by a simulation and analytical study of the proposed solutions to show its performance.